add waveglow #92
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add waveglow #92
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yuzhou03
reviewed
Jun 1, 2023
|
除了tacotron2_common之外，还有哪些文件是copy的未经修改，请comment |
shh2000
reviewed
Jun 2, 2023
shh2000
reviewed
Jun 2, 2023
shh2000
reviewed
Jun 2, 2023
shh2000
reviewed
Jun 2, 2023
shh2000
reviewed
Jun 2, 2023
shh2000
reviewed
Jun 2, 2023
shh2000
reviewed
Jun 2, 2023
shh2000
reviewed
Jun 2, 2023
shh2000
reviewed
Jun 2, 2023
shh2000
reviewed
Jun 2, 2023
shh2000
reviewed
Jun 2, 2023
| jittable=False): | ||
| """ Code chooses a model based on name""" | ||
| model = None | ||
| if model_name == 'Tacotron2': |
shh2000
reviewed
Jun 2, 2023
yuzhou03
reviewed
Jun 2, 2023
yuzhou03
reviewed
Jun 2, 2023
yuzhou03
reviewed
Jun 2, 2023
yuzhou03
reviewed
Jun 2, 2023
yuzhou03
reviewed
Jun 2, 2023
yuzhou03
reviewed
Jun 2, 2023
|
以下文件参考源代码，不需要review： |
yuzhou03
reviewed
Jun 5, 2023
| ### 模型信息 | ||
| - Introduction | ||
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| The WaveGlow model is a flow-based generative model that generates audio samples from Gaussian distribution using mel-spectrogram conditioning (Figure 2). During training, the model learns to transform the dataset distribution into spherical Gaussian distribution through a series of flows. One step of a flow consists of an invertible convolution, followed by a modified WaveNet architecture that serves as an affine coupling layer. During inference, the network is inverted and audio samples are generated from the Gaussian distribution. Our implementation uses 512 residual channels in the coupling layer. |
yuzhou03
reviewed
Jun 5, 2023
yuzhou03
reviewed
Jun 5, 2023
| ### 运行情况 | ||
| | 训练资源 | 配置文件 | 运行时长(s) | 目标val_loss | 收敛val_loss | 性能(samples/s) | | ||
| | -------- | --------------- | ----------- | ------------ | ------------ | --------------- | | ||
| | 单机8卡 | config_A100x1x8 | | | | | |
yuzhou03
reviewed
Jun 5, 2023
yuzhou03
reviewed
Jun 5, 2023
shh2000
approved these changes
Jun 5, 2023
yuzhou03
approved these changes
Jun 5, 2023
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